A spectral method for confidence interval generation and run length control in simulations
Communications of the ACM - Special issue on simulation modeling and statistical computing
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
ASAP3: a batch means procedure for steady-state simulation analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Performance of a Wavelet-Based Spectral Procedure for Steady-State Simulation Analysis
INFORMS Journal on Computing
ASAP3: a batch means procedure for steady-state simulation analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Performance evaluation of spectral procedures for simulation analysis
Proceedings of the 38th conference on Winter simulation
Automating warm-up length estimation
Proceedings of the 40th Conference on Winter Simulation
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We summarize the results of an experimental performance evaluation of WASSP, an automated wavelet-based spectral method for constructing an approximate confidence interval on the steady-state mean of a simulation output process so that the delivered confidence interval satisfies user-specified requirements on absolute or relative precision as well as coverage probability. We applied WASSP to test problems designed specifically to explore its efficiency and robustness in comparison with ASAP3 and the Heidelberger-Welch algorithm, two sequential procedures based respectively on the methods of nonoverlapping batch means and spectral analysis. Concerning efficiency, WASSP compared favorably with its competitors, often requiring smaller sample sizes to deliver confidence intervals with the same nominal levels of precision and coverage probability. Concerning robustness against the statistical anomalies commonly encountered in simulation studies, WASSP outperformed its competitors, delivering confidence intervals whose actual half-lengths and coverage probabilities were frequently closer to the corresponding user-specified nominal levels.